CN112000264B - Dish information display method and device, computer equipment and storage medium - Google Patents

Dish information display method and device, computer equipment and storage medium Download PDF

Info

Publication number
CN112000264B
CN112000264B CN202010789396.8A CN202010789396A CN112000264B CN 112000264 B CN112000264 B CN 112000264B CN 202010789396 A CN202010789396 A CN 202010789396A CN 112000264 B CN112000264 B CN 112000264B
Authority
CN
China
Prior art keywords
dish
information
dishes
historical
target
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202010789396.8A
Other languages
Chinese (zh)
Other versions
CN112000264A (en
Inventor
林宝成
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Sankuai Online Technology Co Ltd
Original Assignee
Beijing Sankuai Online Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Sankuai Online Technology Co Ltd filed Critical Beijing Sankuai Online Technology Co Ltd
Priority to CN202010789396.8A priority Critical patent/CN112000264B/en
Publication of CN112000264A publication Critical patent/CN112000264A/en
Application granted granted Critical
Publication of CN112000264B publication Critical patent/CN112000264B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
    • G06F3/0484—Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/955—Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
    • G06F16/9554—Retrieval from the web using information identifiers, e.g. uniform resource locators [URL] by using bar codes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0623—Item investigation
    • G06Q30/0625—Directed, with specific intent or strategy
    • G06Q30/0629—Directed, with specific intent or strategy for generating comparisons
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/12—Hotels or restaurants

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • General Engineering & Computer Science (AREA)
  • Finance (AREA)
  • Accounting & Taxation (AREA)
  • Databases & Information Systems (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • General Health & Medical Sciences (AREA)
  • Human Resources & Organizations (AREA)
  • Development Economics (AREA)
  • Health & Medical Sciences (AREA)
  • Human Computer Interaction (AREA)
  • Primary Health Care (AREA)
  • Data Mining & Analysis (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The application discloses a method and a device for displaying dish information, computer equipment and a storage medium, and belongs to the technical field of big data. The method comprises the following steps: the method comprises the steps of obtaining dish sales information and dish price information of a plurality of dishes of a target merchant, determining target prediction results of the plurality of dishes based on the dish sales information and the dish price information of the plurality of dishes, determining dish information display sequences of the plurality of dishes based on the target prediction results of the plurality of dishes, and returning the dish information display sequences of the plurality of dishes to a terminal for display. According to the method and the device, through analyzing the dish sales information and the dish price information of each dish in the target merchant, the prediction result used for expressing the resource quantity corresponding to the sales volume is predicted, the optimal dish information display sequence can be determined according to the target prediction result, and then the dish information is displayed based on the dish information display sequence, so that when a consumer orders a meal, the dish which wants to be ordered can be found in a short time, and the man-machine interaction efficiency is improved.

Description

Dish information display method and device, computer equipment and storage medium
Technical Field
The present application relates to the field of big data technologies, and in particular, to a method and an apparatus for displaying dish information, a computer device, and a storage medium.
Background
With the development of mobile internet, electronic menus gradually become a novel dish ordering tool in the catering industry. When a consumer orders a meal, the two-dimensional code of a merchant can be scanned by the terminal, and after the two-dimensional code is scanned, dish information of the merchant can be displayed on a terminal interface. For the merchant, it is very important how to design the dish information display sequence of each dish to attract as many consumers as possible to place orders.
At present, the dish information display method generally comprises the following steps: the merchant can manually set the display sequence of the information of each dish in advance, and then display the information of each dish based on the display sequence.
In the technology, the merchant determines the display sequence of the dish information only by individual subjectivity, so that the problem that the display sequence is not principle and irregular is easily caused, and the follow-up consumer needs to spend a long time to find the dish which the merchant wants to order when ordering the dish based on the electronic menu of the display sequence, thereby reducing the man-machine interaction efficiency.
Disclosure of Invention
The embodiment of the application provides a dish information display method and device, computer equipment and a storage medium, so that when a consumer orders a meal, the consumer can find dishes to be ordered in a short time, and the man-machine interaction efficiency is improved. The technical scheme is as follows:
in one aspect, a method for displaying dish information is provided, and the method comprises the following steps:
responding to a dish information acquisition request of a terminal to a target merchant, and acquiring dish sales information and dish price information of a plurality of dishes of the target merchant;
determining a target prediction result of the plurality of dishes based on the dish sales information and the dish price information of the plurality of dishes, the target prediction result being used for representing the quantity of resources obtained based on the sales amount of the dishes;
determining a dish information display sequence of the plurality of dishes based on the target prediction results of the plurality of dishes;
and returning the dish information display sequence of the plurality of dishes to the terminal for displaying.
In one possible implementation, the determining the target prediction results of the plurality of dishes based on the dish sales information and the dish price information of the plurality of dishes comprises:
for each dish in the plurality of dishes, inputting dish sales information and dish price information of the dish into a target model, and performing feature extraction on the dish sales information and the dish price information through the target model to obtain dish sales features and dish price features;
splicing the price characteristic of the dish and the price characteristic of the dish to obtain the target dish characteristic;
predicting the target dish feature to obtain a resource quantity feature corresponding to the target dish feature;
and determining a target prediction result of the dish based on the resource quantity characteristic.
In one possible implementation, the determining process of the target model includes:
obtaining historical dish sales information, historical dish price information and sample resource quantity of a plurality of dishes;
and performing model training based on historical dish sales information, historical dish price information and sample resource quantity of the plurality of dishes to obtain the target model.
In a possible implementation manner, the performing model training based on historical dish sales information of the plurality of dishes, historical dish price information, and sample resource quantities of the plurality of dishes to obtain the target model includes:
in an iteration process, inputting the historical dish sales information and the historical dish price information into an initial model to obtain a training result of the iteration process;
determining a loss function based on the training result of the iteration process and the sample resource quantity;
and adjusting the model parameters of the initial model based on the loss function until the training meets the target condition, and acquiring the model corresponding to the iterative process meeting the target condition as the target model.
In one possible implementation, the training process of the target model includes:
and performing model training based on historical dish sales information and historical dish price information of the plurality of dishes, the sample resource quantity of the plurality of dishes and a linear regression algorithm to obtain the target model.
In one possible implementation, the training process of the target model includes:
and performing model training based on historical dish sales information and historical dish price information of the plurality of dishes, the sample resource quantity of the plurality of dishes and a deep learning algorithm to obtain the target model.
In one possible implementation, the training process of the target model includes:
and performing model training based on historical dish sales information and historical dish price information of the plurality of dishes, the sample resource quantity of the plurality of dishes and a clustering algorithm to obtain the target model.
In one possible implementation, the training process of the target model includes any one of:
performing model training based on historical dish sales information and historical dish price information of the plurality of dishes, sample resource quantity of the plurality of dishes and an online algorithm to obtain the target model;
and performing model training based on historical dish sales information and historical dish price information of the plurality of dishes, sample resource quantity of the plurality of dishes and an off-line algorithm to obtain the target model.
In one possible implementation, the determining a dish information display order of the plurality of dishes based on the target prediction results of the plurality of dishes includes:
and determining the dish information display sequence of the dishes based on the numerical values of the target prediction results of the dishes.
In another aspect, there is provided a dish information display apparatus, the apparatus including:
the acquisition module is used for responding to a dish information acquisition request of a terminal to a target merchant and acquiring dish sales information and dish price information of a plurality of dishes of the target merchant;
a prediction result determining module for determining a target prediction result of the plurality of dishes based on the dish sales information and the dish price information of the plurality of dishes, the target prediction result being used for representing the amount of resources obtained based on the sales amount of the dishes;
the display sequence determining module is used for determining the display sequence of the dish information of the plurality of dishes based on the target prediction results of the plurality of dishes;
and the returning module is used for returning the dish information display sequence of the plurality of dishes to the terminal for displaying.
In one possible implementation, the prediction result determining module is configured to:
for each dish in the plurality of dishes, inputting dish sales information and dish price information of the dish into a target model, and performing feature extraction on the dish sales information and the dish price information through the target model to obtain dish sales features and dish price features;
splicing the price characteristic of the dish and the price characteristic of the dish to obtain the target dish characteristic;
predicting the target dish feature to obtain a resource quantity feature corresponding to the target dish feature;
and determining a target prediction result of the dish based on the resource quantity characteristic.
In one possible implementation, the determining process of the target model includes:
obtaining historical dish sales information, historical dish price information and sample resource quantity of a plurality of dishes;
and performing model training based on historical dish sales information, historical dish price information and sample resource quantity of the plurality of dishes to obtain the target model.
In one possible implementation manner, performing model training based on historical dish sales information and historical dish price information of the plurality of dishes and sample resource quantities of the plurality of dishes, and obtaining the target model includes:
in an iteration process, inputting the historical dish sales information and the historical dish price information into an initial model to obtain a training result of the iteration process;
determining a loss function based on the training result of the iteration process and the sample resource quantity;
and adjusting the model parameters of the initial model based on the loss function until the training meets the target condition, and acquiring the model corresponding to the iterative process meeting the target condition as the target model.
In one possible implementation, the training process of the target model includes:
and performing model training based on historical dish sales information and historical dish price information of the plurality of dishes, the sample resource quantity of the plurality of dishes and a linear regression algorithm to obtain the target model.
In one possible implementation, the training process of the target model includes:
and performing model training based on historical dish sales information and historical dish price information of the plurality of dishes, the sample resource quantity of the plurality of dishes and a deep learning algorithm to obtain the target model.
In one possible implementation, the training process of the target model includes:
and performing model training based on historical dish sales information and historical dish price information of the plurality of dishes, the sample resource quantity of the plurality of dishes and a clustering algorithm to obtain the target model.
In one possible implementation, the training process of the target model includes any one of:
performing model training based on historical dish sales information and historical dish price information of the plurality of dishes, sample resource quantity of the plurality of dishes and an online algorithm to obtain the target model;
and performing model training based on historical dish sales information and historical dish price information of the plurality of dishes, the sample resource quantity of the plurality of dishes and an offline algorithm to obtain the target model.
In one possible implementation manner, the presentation order determining module is configured to:
and determining the dish information display sequence of the dishes based on the numerical values of the target prediction results of the dishes.
In one aspect, a computer device is provided and includes one or more processors and one or more memories having at least one instruction stored therein, the instruction being loaded and executed by the one or more processors to implement the operations performed by the dish information presentation method.
In one aspect, a computer-readable storage medium is provided, in which at least one instruction is stored, and the instruction is loaded and executed by a processor to implement the operations performed by the dish information presentation method.
According to the technical scheme, the prediction result used for expressing the resource quantity corresponding to the sales volume is predicted by analyzing the dish sales information and the dish price information of each dish in the target business, the optimal dish information display sequence can be determined according to the target prediction result, and then the dish information is displayed based on the dish information display sequence, so that when a consumer orders a meal, the dish to be ordered can be found in a short time, and the human-computer interaction efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of an implementation environment of a method for displaying dish information according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a method for displaying dish information according to an embodiment of the present disclosure;
fig. 3 is a flowchart of a method for displaying dish information according to an embodiment of the present disclosure;
fig. 4 is an exemplary flowchart of a method for displaying menu information provided by an embodiment of the present application;
fig. 5 is a schematic structural diagram of a dish information display device according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a terminal according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a server according to an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, the following detailed description of the embodiments of the present application will be made with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of an implementation environment of a method for displaying dish information according to an embodiment of the present application. Referring to fig. 1, the implementation environment includes: a terminal 101 and a server 102.
The terminal 101 may be at least one of a smartphone, a smart watch, a desktop computer, a laptop computer, a virtual reality terminal, an augmented reality terminal, a wireless terminal, a laptop portable computer, and the like. The terminal 101 may be installed with an ordering application program, a takeaway application program, and the like, when a user wants to order, the user may log in the ordering application program or the takeaway application program, find a tag corresponding to a merchant who wants to order in the ordering application program or the takeaway application program, and click the tag corresponding to the merchant, so that an electronic menu of the merchant may be displayed on an interface of the terminal 101, and the user may click a dish that wants to order, and then perform a subsequent ordering payment operation. The terminal 101 may have a scanning function, and when a user wants to order, the terminal 101 may also be used to scan an ordering two-dimensional code of the merchant store, where the ordering two-dimensional code is used to instruct to obtain an electronic menu of the merchant store, and then the electronic menu of the merchant may be displayed on an interface of the terminal 101, and the user may click a dish that the user wants to order, and then perform a subsequent ordering payment operation. The terminal 101 has a communication function and can access the internet, and the terminal 101 may be generally referred to as one of a plurality of terminals, and this embodiment is only illustrated by the terminal 101. Those skilled in the art will appreciate that the number of terminals described above may be greater or fewer.
The server 102 may be an independent physical server, a server cluster or a distributed file system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like. The server 102 and the terminal 101 may be directly or indirectly connected through wired or wireless communication, which is not limited in this embodiment of the application. Alternatively, the number of the servers 102 may be more or less, and the embodiment of the present application is not limited thereto. Of course, the server 102 may also include other functional servers to provide more comprehensive and diverse services.
In the process of implementing the embodiment of the application, when a user wants to order in a merchant, the terminal 101 may be used to scan an ordering two-dimensional code of the merchant, after the terminal 101 scans the ordering two-dimensional code, a process of sending a dish information acquisition request to the server 102 is triggered, and then the server responds to the dish information acquisition request, determines a dish information display sequence of a plurality of dishes by using the dish information display method provided by the embodiment of the application, and returns the dish information display sequence of the plurality of dishes to the terminal 101, and after the terminal 101 receives the dish information display sequence of the plurality of dishes, the plurality of dishes are displayed on the terminal 101 based on the dish information display sequence. The target merchant is subsequently adopted in the embodiment of the application to represent the merchant that the user wants to order.
Fig. 2 is a flowchart of a method for displaying menu information according to an embodiment of the present disclosure, where the embodiment is described with a server as an execution subject. Referring to fig. 2, the embodiment includes:
201. the server responds to a dish information acquisition request of the terminal to a target merchant, and acquires dish sales information and dish price information of a plurality of dishes of the target merchant.
202. The server determines a target prediction result of the plurality of dishes based on the dish sales information and the dish price information of the plurality of dishes, the target prediction result being used for representing the amount of resources obtained based on the sales amount of the dishes.
203. The server determines a dish information display order of the plurality of dishes based on the target prediction results of the plurality of dishes.
204. And the server returns the dish information display sequence of the plurality of dishes to the terminal for displaying.
According to the technical scheme, the prediction result used for expressing the resource quantity corresponding to the sales volume is predicted by analyzing the dish sales information and the dish price information of each dish in the target business, the optimal dish information display sequence can be determined according to the target prediction result, and then the dish information is displayed based on the dish information display sequence, so that when a consumer orders a meal, the dish to be ordered can be found in a short time, and the human-computer interaction efficiency is improved.
Fig. 3 is a flowchart of a method for displaying dish information according to an embodiment of the present application. Referring to fig. 3, the embodiment includes:
301. and the terminal sends a dish information acquisition request for the target merchant to the server.
The target merchant can be merchants of all industries, and in the embodiment of the application, the target merchant can be a merchant of the catering industry, so that the aim of displaying the menu corresponding to the target merchant is fulfilled. Alternatively, the target merchant may be a merchant store or an online merchant registered in the ordering application. The dish information obtaining request may carry a merchant identifier of the target merchant.
In one possible implementation manner, when the user wants to order a meal, the terminal can be operated to send a dish information acquisition request for the target merchant to the server. For example, when a user wants to order in a target merchant, the terminal may be used to scan an ordering two-dimensional code of the target merchant, where the ordering two-dimensional code is used to instruct to acquire an electronic menu of the target merchant, and after the terminal scans the ordering two-dimensional code, a process of sending a dish information acquisition request to the server is triggered. For another example, when the user wants to order food in the target merchant, the user may operate on the terminal, log in the ordering application program, find the tag corresponding to the target merchant in the ordering application program interface, click the target merchant, trigger the dish information acquisition instruction, and send the dish information acquisition request to the server by the terminal in response to the dish information acquisition instruction. In addition, the user can also trigger the process that the terminal sends the dish information acquisition request to the server according to the ordering operation indicated by the public number or the applet by paying attention to the public number or the applet of the target merchant. The embodiment of the application does not limit how the terminal is triggered to send the dish information acquisition request.
302. The server responds to a dish information acquisition request of the terminal to a target merchant, and acquires dish sales information and dish price information of a plurality of dishes of the target merchant.
In the embodiment of the application, the dish sales information and the dish price information are selected as relevant information required by prediction to perform a subsequent prediction process. The dish selling information may include information on sales volume, sales price, sales time, etc. of a plurality of dishes in the target merchant, and the dish price information may include prices of the plurality of dishes in the target merchant at the current time.
Optionally, the server may further obtain order information of the dishes, inventory information of the dishes, offer information of the dishes, and the like to perform a subsequent prediction process, and the embodiment of the present application does not limit what kind of relevant information of the dishes is selected. The following description will be given only by taking the dish sales information and the dish price information as examples.
In a possible implementation manner, a server receives a dish information acquisition request sent by a terminal, acquires a merchant identifier of a target merchant carried by the dish information acquisition request, and queries in a merchant information base based on the merchant identifier to obtain dish sales information and dish price information of a plurality of dishes corresponding to the merchant identifier, wherein the merchant information base is used for storing dish related information such as dish sales information and dish price information of a plurality of merchants and a plurality of dishes in the merchants.
Optionally, the server may update the dish sales information in real time based on the sales situation of each merchant, and the corresponding process may be: and if the server receives a dish ordering request sent by the terminal, determining a dish corresponding to the dish ordering request according to a dish identification carried by the dish ordering request, and increasing corresponding sales in the dish sales information of the dish to obtain updated dish sales information. Similarly, the server may also update the dish price information in real time based on the dish price change condition of each merchant, and the corresponding process may be: and if the server receives a dish price updating request sent by the terminal, acquiring a dish identifier and an updating price carried by the dish price updating request, determining a dish corresponding to the dish price updating request according to the dish identifier, and changing in the dish price information of the dish according to the updating price to obtain updated dish price information. The dish identifier may be preset by a technician, such as an initial of a dish name, or a serial number of a dish.
303. The server determines a target prediction result of the plurality of dishes based on the dish sales information and the dish price information of the plurality of dishes, the target prediction result being indicative of a resource amount obtained based on a sales amount of the dishes.
The target prediction result is used to indicate the number of resources obtained based on the sales volume of the dish, and may be understood as a return prediction result. The target prediction result can be represented by a score obtained by scoring the number of resources. In one possible implementation, the target prediction result may be represented by a larger fraction and a larger number of sales resources. In another possible implementation manner, the target prediction result may also be represented in a manner that the smaller the score is, the larger the sales resource amount is. The resource quantity is used for representing the return of benefits corresponding to the sales volume. Alternatively, the resource amount may be a customer price, an order amount, or the like, wherein the customer price refers to an average amount of consumption of each customer in the target merchant.
In a possible implementation manner, for each dish in the plurality of dishes, the server inputs dish sales information and dish price information of the dish into a target model, performs feature extraction on the dish sales information and the dish price information through the target model to obtain dish sales features and dish price features, splices the dish price features and the dish price features to obtain target dish features, analyzes the target dish features to obtain resource quantity features corresponding to the target dish features, and determines a target prediction result of the dish based on the resource quantity features. Wherein the target model is used to represent a network model that is capable of predicting the amount of resources.
Optionally, the technician may select historical dish sales information, historical dish price information, and resource quantity of the plurality of dishes corresponding to the plurality of dishes of one or more merchants in advance, mark the plurality of dishes in scores according to the resource quantity of the plurality of dishes, or perform normalization processing on the resource quantity of the plurality of dishes by using the terminal, thereby obtaining sample resource quantity corresponding to the plurality of dishes. Wherein, the sample resource quantity is used for representing the real resource quantity used in the model training. In addition, a technician can input historical dish sales information, historical dish price information and sample resource quantity of the plurality of dishes corresponding to the plurality of dishes into the server, and then the server can obtain the historical dish sales information, the historical dish price information and the sample resource quantity of the plurality of dishes. And the server performs model training based on the historical dish sales information, the historical dish price information and the sample resource quantity of the plurality of dishes to obtain the target model. The model training process may be as follows: in an iteration process, inputting the historical dish sales information and the historical dish price information into an initial model to obtain a training result of the iteration process, determining a loss function based on the training result of the iteration process and the sample resource quantity, adjusting model parameters of the initial model based on the loss function until the training meets a target condition, and acquiring a model corresponding to the iteration process meeting the target condition as the target model. Specifically, the method comprises the following steps: and in the first iteration process, inputting the historical dish sales information and the historical dish price information into an initial model to obtain a training result of the first iteration process. And determining a loss function based on the training result of the first iteration process and the number of sample resources, and adjusting model parameters in the initial model based on the loss function. And taking the model parameters after the first iteration adjustment as the model parameters of the second iteration, and then carrying out the second iteration. And repeating the iteration process for a plurality of times, in the Nth process, taking the model parameters after the N-1 th iteration adjustment as new model parameters, carrying out model training until the training meets the target conditions, and acquiring the model corresponding to the iteration process meeting the target conditions as the target model. Optionally, the target condition met by the training may be that the number of training iterations of the initial model reaches the target number, and a technician may preset the number of training iterations. Alternatively, the target condition met by the training may be that the loss value meets a target threshold condition, such as a loss value less than 0.00001. The embodiments of the present application do not limit this. Wherein N is a positive integer and is greater than 1.
In the training process of the target model, the server may perform model training based on a Deep Learning (Deep Learning) algorithm, that is, the server performs model training based on historical dish sales information and historical dish price information of the plurality of dishes, the number of sample resources of the plurality of dishes, and the Deep Learning algorithm to obtain the target model.
The deep learning algorithm is a machine learning algorithm with an artificial neural network as a framework. Optionally, the specific process of performing model training based on the deep learning algorithm may be as follows: according to training data (namely historical dish sales information and historical dish price information) input by technicians, the structural hierarchy of a deep learning model can be set, an untrained deep learning model is constructed according to the structural hierarchy, namely an initial model is constructed, historical dish sales information and historical dish price information of a plurality of dishes are input into the initial model, then training results of the plurality of dishes are output, and then the processes of subsequent iterative training and loss function minimization are carried out, so that the deep learning model meeting target conditions is obtained and serves as a target model. For example, the deep learning model may include an input layer, a hidden layer, and an output layer, where the input layer may only include one layer, the input layer is configured to perform preprocessing such as mean value removal and normalization on input features to standardize the input features, the hidden layer may include a plurality of feature extraction layers to extract dish sale features and dish price features corresponding to a plurality of dishes, and the hidden layer is further configured to perform splicing processing on the dish sale features and the dish price features to obtain target dish features. The output layer can be a full connection layer and is used for carrying out weighted average calculation on the target dish features extracted by the hidden layer to obtain regression features, and then classifying the dishes according to the regression features. For example, the target model trained based on the deep learning algorithm may be a Convolutional Neural Network (CNN), which is a type of Neural network that includes convolution calculations and has a deep structure.
In the step 303, taking the target model as the model obtained by training based on the deep learning algorithm as an example, in another possible implementation method, the server may also perform model training based on the historical dish sales information and historical dish price information of the plurality of dishes, the sample resource quantity of the plurality of dishes, and a linear regression algorithm to obtain the target model.
Among other things, linear regression algorithms are used to determine the linear relationship between a target (dependent variable) and one or more predicted variables (independent variables), and the linear relationship between the target and one or more predicted variables is typically established by fitting a best straight line, which may be referred to as a regression line and may be represented by linear equation (1).
Figure BDA0002623208830000111
In the formula (I), the compound is shown in the specification,
Figure BDA0002623208830000112
to the target, x1Is a first predictor variable, x2Is a second predictor variable, xnIn order for the n-th predicted variable,
Figure BDA0002623208830000113
the ith first predictor variable, xi2Ith first predictor variable, wiAnd b is a hyper-parameter (i.e. the above-mentioned model parameter), w1For the hyper-parameter corresponding to the first predictor variable, w2And the hyperparameter corresponds to the second prediction variable. After determining the linear relationship between the target and the one or more predictors, the target may be predicted based on the one or more predictors. In the embodiment of the application, the target can be the quantity of the resource, and the prediction variable can be dish sales information and dish price information. It should be understood that the embodiments of the present application employ a multiple linear regression algorithm, and multiple regression or curve regression can be obtained by fitting. Optionally, the specific process of performing model training based on the linear regression algorithm may be: based on the linear equation (1), an untrained regression model is constructed, namely an initial model is constructed, and historical dish sales information of the plurality of dishes is sentAnd inputting information and historical dish price information into the initial model, outputting training results of the plurality of dishes, and performing subsequent iterative training and a process of minimizing a loss function, thereby obtaining a regression model meeting target conditions as a target model.
In another possible implementation method, the server may further perform model training based on historical dish sales information and historical dish price information of the plurality of dishes, the number of sample resources of the plurality of dishes, and a clustering algorithm to obtain the target model.
The clustering algorithm may also be referred to as a K-means algorithm, which measures the similarity between different features by distance to classify the features into different feature classes, where the features in the same feature class have great similarity. In the embodiment of the application, the dish selling characteristics and the dish price characteristics corresponding to a plurality of dishes are classified by adopting a clustering algorithm to obtain a plurality of characteristic categories, so that the characteristic similarity in each characteristic category is higher, and the characteristic similarity in different characteristic categories is weaker. Optionally, the server may select different distances for clustering, for example, an euclidean distance, a hamming distance, a manhattan distance, a chi-square distance, a chebyshev distance, and the like, and the embodiment of the present disclosure does not limit what distance is selected. Optionally, the specific process of performing model training based on the clustering algorithm may be: and constructing a target model based on a clustering algorithm, inputting historical dish sales information and historical dish price information of the plurality of dishes into the target model, clustering the target dish features of the plurality of dishes after extracting the target dish features of the plurality of dishes, determining feature types corresponding to the target dish features of the plurality of dishes respectively, and determining target prediction results corresponding to the plurality of dishes based on the feature types of the plurality of dishes.
It should be noted that, the above three algorithms for model training are provided, and in the process of performing model training based on any one of the above algorithms, the server may perform model training by using any one of the following online algorithms or offline algorithms.
In a possible implementation method, the server may perform model training based on historical dish sales information and historical dish price information of the plurality of dishes, sample resource quantities of the plurality of dishes, and an online algorithm to obtain the target model.
Where the online algorithm can process the inputs one by one in a serialized manner, without knowing all of the inputs at an initial time. For example, the insertion ordering is one of online algorithms, and the corresponding process may be: when the data to be ordered is acquired, the size of the data to be ordered and a plurality of data in the ordered sequence are judged, the position of the data to be ordered in the ordered sequence is determined, the data to be ordered is placed at the corresponding position, and then the ordered sequence after ordering can be acquired. In this embodiment of the application, the data to be sorted may be target prediction results corresponding to a plurality of dishes, and based on a principle that the maximum (or minimum) sorting of the target prediction results is closer to the front, the data to be sorted is continuously inserted into the ordered sequence to arrange the plurality of dishes, so that the ordered sequence after sorting can be obtained to determine the dish information display order of the plurality of dishes. By adopting the online algorithm, when the sales information and the price information of the dishes corresponding to an input dish are received, the display information corresponding to the dish can be determined in real time, the problem that the display sequence is inaccurate because the display sequence of a newly added dish cannot be determined when a new dish is added is solved, and the accuracy of determining the display sequence is improved.
In another possible implementation method, the server may also perform model training based on historical dish sales information and historical dish price information of the plurality of dishes, the sample resource quantity of the plurality of dishes, and an offline algorithm to obtain the target model.
The off-line algorithm needs to know all inputs at the initial moment, and outputs a result immediately after processing one input. For example, the selection ordering is one of the off-line algorithms, and the corresponding process may be: after all the data to be sorted are known, the maximum (or minimum) data are determined from the data to be sorted, the data are placed at the initial position of the ordered sequence, the maximum (or minimum) data are continuously determined from the data to be sorted, the data to be sorted are sequentially arranged, and then the ordered sequence after sorting can be obtained. In this embodiment of the application, the data to be sorted may be target prediction results corresponding to a plurality of dishes, and the dishes are sequentially arranged based on a principle that the maximum (or minimum) numerical value of the target prediction results is closer to the front, so that a sorted ordered sequence may be obtained as a dish information display order of the dishes.
It should be appreciated that the above online and offline algorithms may be applied in conjunction with any of a linear regression algorithm, a deep learning algorithm, or a clustering algorithm. For example, an online algorithm is adopted in a target model obtained based on linear regression algorithm training, and the dish information display sequence of a plurality of dishes can be directly output.
304. The server determines a dish information display order of the plurality of dishes based on the target prediction results of the plurality of dishes.
The display order of the dish information is used for indicating the display order of the dish information corresponding to each dish, and the dish information is used for indicating the dish information displayed in the electronic menu, for example, the dish information may include information such as a name of a dish corresponding to the dish, a price of the dish, a picture of the dish, a preference of the dish, and the like.
In one possible implementation method, the server may determine the dish information display order of the plurality of dishes based on the numerical value of the target prediction results of the plurality of dishes. Optionally, the specific process of the server determining the dish information display sequence may be: and according to the numerical values of the target prediction results of the plurality of dishes, taking the dish with the high numerical value of the target prediction result as the dish with the front ranking, taking the dish with the low numerical value of the target prediction result as the dish with the back ranking, and further obtaining the dish information display sequence of the plurality of dishes. In another possible implementation method, the lower the value of the target prediction result is, the more the resource quantity is, the more the display order is, that is, according to the value of the target prediction results of the plurality of dishes, the lower the value of the target prediction result is, the more the display order is, that is, according to the value of the target prediction results of the plurality of dishes, the dish with the low value of the target prediction result is taken as the dish with the top ranking, the dish with the high value of the target prediction result is taken as the dish with the bottom ranking, and then the dish information display order of the plurality of dishes is obtained.
Optionally, the order of displaying the dish information may be represented by a corresponding relationship between the dish identifier and the order, the corresponding relationship may be in a form of a table, as shown in table 1, and after the server determines the order of displaying the dish information, the order of the dish identifier corresponding to the dish identifier may be obtained. In another possible implementation method, the order of displaying the dish information may be represented by an ordered sequence, and the ordered sequence may be an ordered sequence of a plurality of dish identifiers, for example, the ordered sequence may be rdacybllc, and it may be found that the order corresponding to the dish identifier a is 3, the order corresponding to the dish identifier B is 6, and the order corresponding to the dish identifier C is 9.
TABLE 1
Dish identification Sequence of
A 3
B 6
C 9
305. And the server returns the dish information display sequence and the dish information of the plurality of dishes to the terminal.
In a possible implementation method, after determining the display sequence of the dish information of the plurality of dishes, the server determines the dish information of the plurality of dishes based on the display sequence of the dish information of the plurality of dishes, and returns the display sequence of the dish information of the plurality of dishes and the dish information to the terminal to trigger the terminal to perform a subsequent display process.
Alternatively, the process of the server determining the dish information of a plurality of dishes may be: the server obtains a plurality of dish identifications carried by the dish information display sequence after determining the dish information display sequence of the plurality of dishes, and respectively determines the dish information corresponding to the plurality of dish identifications according to the corresponding relations between the plurality of dish identifications and the dish information, so as to obtain a plurality of dish information corresponding to the plurality of dish identifications. The server stores the corresponding relation between the dish identification and the dish information.
306. The terminal receives the dish information display sequence and the dish information of the plurality of dishes, and displays the dish information of the plurality of dishes based on the dish information display sequence of the plurality of dishes.
In a possible implementation method, after receiving the dish information display sequence and the dish information of the plurality of dishes, the terminal displays the dish information based on the dish information display sequence corresponding to each piece of dish information.
Optionally, if the order of displaying the dish information is represented by a corresponding relationship between the dish identifiers and the order, after the terminal receives the order of displaying the dish information of the plurality of dishes and the dish information, the terminal may determine the order of displaying the dish information corresponding to each piece of dish information to be displayed, and then display the piece of dish information to be displayed according to the order of displaying the dish information corresponding to each piece of dish information to be displayed. In another possible implementation method, if the display order of the dish information is represented by an ordered sequence, after receiving the display order of the dish information of the plurality of dishes, the terminal determines the dish information corresponding to the order from front to back, and displays the dish information in sequence. In the above steps 304 to 306, after the server determines the display order of the dish information, the display order of the dish information is sent to the terminal, and the terminal displays a plurality of pieces of dish information based on the display order of the dish information. In another possible implementation method, after determining the display order of the dish information, the server sends the display order of the dish information to the terminal, and the terminal generates the display logic and style of the electronic menu based on the display order of the dish information and the plurality of pieces of dish information to obtain a target electronic menu and displays the target electronic menu. The embodiment of the present application does not limit the determination of the execution subject of the electronic menu.
For example, fig. 4 is an exemplary flowchart of a method for displaying menu information according to an embodiment of the present application, and as shown in fig. 4, the method for displaying menu information may be implemented by the following three steps, where in a first step, the terminal scans the two-dimensional code, triggers an instruction for obtaining an electronic menu, the terminal sends a request for obtaining a menu display order rule to the server based on the instruction, the server receives the request, and executes a process of determining an optimal customer price display order based on a merchant menu sales volume, a merchant menu order, and a merchant menu price in fig. 4 based on the request, the optimal customer price display order is used as the menu display order rule, and the menu display order rule is output to the terminal, so that after receiving the menu display order rule, the terminal displays a plurality of menu information based on the menu display order rule, and achieves an effect of displaying the electronic menu.
According to the technical scheme, the method and the system for displaying the dish sales information of each dish in the target business are used for predicting the target prediction result for representing the resource quantity corresponding to the sales volume by analyzing the dish sales information and the dish price information of each dish in the target business, the dish information display sequence is determined according to the target prediction result, the optimal display sequence can be determined, and then the dish information is displayed based on the display sequence, so that when a consumer orders a meal, the dish to be ordered can be found in a short time, the man-machine interaction efficiency is improved, the consumer can be attracted to order as many as possible, the sales volume of the business is increased, and great business benefits are brought. According to the method and the device for displaying the dish information, the dish selling information and the dish price information of the commercial tenant are analyzed through big data and machine learning, the optimal dish information displaying sequence capable of improving the unit price of customers can be predicted, and then the electronic menu generated according to the optimal dish information displaying sequence is more consistent with the purchasing tendency of consumers, and the possibility of purchasing of the users can be improved.
All the above optional technical solutions may be combined arbitrarily to form optional embodiments of the present application, and are not described herein again.
Fig. 5 is a schematic structural diagram of a vegetable information display device according to an embodiment of the present application. Referring to fig. 5, the apparatus includes:
an obtaining module 501, configured to respond to a dish information obtaining request from a terminal to a target merchant, and obtain dish sales information and dish price information of multiple dishes of the target merchant;
a prediction result determining module 502, configured to determine a target prediction result of the plurality of dishes based on the dish sales information and the dish price information of the plurality of dishes, where the target prediction result is used to indicate a resource amount obtained based on a sales amount of the dishes;
a display order determining module 503, configured to determine a display order of the dish information of the plurality of dishes based on the target prediction results of the plurality of dishes;
a returning module 504, configured to return the dish information display sequence of the multiple dishes to the terminal for displaying.
In a possible implementation manner, the prediction result determining module 502 is configured to:
for each dish in the plurality of dishes, inputting dish sales information and dish price information of the dish into a target model, and performing feature extraction on the dish sales information and the dish price information through the target model to obtain dish sales features and dish price features;
splicing the price characteristic of the dish and the price characteristic of the dish to obtain the target dish characteristic;
predicting the target dish feature to obtain a resource quantity feature corresponding to the target dish feature;
and determining a target prediction result of the dish based on the resource quantity characteristic.
In one possible implementation, the determining process of the target model includes:
obtaining historical dish sales information, historical dish price information and sample resource quantity of a plurality of dishes;
and performing model training based on historical dish sales information, historical dish price information and sample resource quantity of the plurality of dishes to obtain the target model.
In one possible implementation manner, performing model training based on historical dish sales information and historical dish price information of the plurality of dishes and sample resource quantities of the plurality of dishes, and obtaining the target model includes:
in an iteration process, inputting the historical dish sales information and the historical dish price information into an initial model to obtain a training result of the iteration process;
determining a loss function based on the training result of the iteration process and the sample resource quantity;
and adjusting the model parameters of the initial model based on the loss function until the training meets the target condition, and acquiring the model corresponding to the iterative process meeting the target condition as the target model.
In one possible implementation, the training process of the target model includes:
and performing model training based on historical dish sales information and historical dish price information of the plurality of dishes, the sample resource quantity of the plurality of dishes and a linear regression algorithm to obtain the target model.
In one possible implementation, the training process of the target model includes:
and performing model training based on historical dish sales information and historical dish price information of the plurality of dishes, the sample resource quantity of the plurality of dishes and a deep learning algorithm to obtain the target model.
In one possible implementation, the training process of the target model includes:
and performing model training based on historical dish sales information and historical dish price information of the plurality of dishes, the sample resource quantity of the plurality of dishes and a clustering algorithm to obtain the target model.
In one possible implementation, the training process of the target model includes any one of:
performing model training based on historical dish sales information and historical dish price information of the plurality of dishes, sample resource quantity of the plurality of dishes and an online algorithm to obtain the target model;
and performing model training based on historical dish sales information and historical dish price information of the plurality of dishes, the sample resource quantity of the plurality of dishes and an offline algorithm to obtain the target model.
In a possible implementation manner, the presentation order determining module 503 is configured to:
and determining the dish information display sequence of the dishes based on the numerical values of the target prediction results of the dishes.
According to the technical scheme, the method and the system for displaying the dish sales information of each dish in the target business are used for predicting the target prediction result for representing the resource quantity corresponding to the sales volume by analyzing the dish sales information and the dish price information of each dish in the target business, the dish information display sequence is determined according to the target prediction result, the optimal display sequence can be determined, and then the dish information is displayed based on the display sequence, so that when a consumer orders a meal, the dish to be ordered can be found in a short time, the man-machine interaction efficiency is improved, the consumer can be attracted to order as many as possible, the sales volume of the business is increased, and great business benefits are brought.
It should be noted that: in the dish information display device provided in the above embodiment, when displaying the dish information, only the division of the functional modules is exemplified, and in practical applications, the function distribution may be completed by different functional modules according to needs, that is, the internal structure of the server is divided into different functional modules to complete all or part of the functions described above. In addition, the dish information display device provided by the above embodiment and the dish information display method embodiment belong to the same concept, and the specific implementation process is described in the method embodiment, and is not described herein again.
A computer device in this embodiment may be provided as a terminal, and fig. 6 shows a block diagram of a terminal 600 provided in an exemplary embodiment of this application. The terminal 600 may be: a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III, motion video Experts compression standard Audio Layer 3), an MP4 player (Moving Picture Experts Group Audio Layer IV, motion video Experts compression standard Audio Layer 4), a notebook computer, or a desktop computer. The terminal 600 may also be referred to by other names such as user equipment, portable terminal, laptop terminal, desktop terminal, etc.
In general, the terminal 600 includes: a processor 601 and a memory 602.
The processor 601 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and so on. The processor 601 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 601 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 601 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content that the display screen needs to display. In some embodiments, processor 601 may also include an AI (Artificial Intelligence) processor for processing computational operations related to machine learning.
The memory 602 may include one or more computer-readable storage media, which may be non-transitory. The memory 602 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in the memory 602 is used to store at least one instruction for execution by the processor 601 to implement the dish information presentation method provided by the method embodiments in the present application.
In some embodiments, the terminal 600 may further optionally include: a peripheral interface 603 and at least one peripheral. The processor 601, memory 602 and peripherals interface 603 may be connected by buses or signal lines. Various peripheral devices may be connected to the peripheral interface 603 via a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 604, a display 605, a camera assembly 606, an audio circuit 607, a positioning component 608, and a power supply 609.
The peripheral interface 603 may be used to connect at least one peripheral related to I/O (Input/Output) to the processor 601 and the memory 602. In some embodiments, the processor 601, memory 602, and peripheral interface 603 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 601, the memory 602, and the peripheral interface 603 may be implemented on separate chips or circuit boards, which is not limited by the present embodiment.
The Radio Frequency circuit 604 is used for receiving and transmitting RF (Radio Frequency) signals, also called electromagnetic signals. The radio frequency circuitry 604 communicates with a communication network and other communication devices via electromagnetic signals. The rf circuit 604 converts an electrical signal into an electromagnetic signal to be transmitted, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 604 comprises: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and so forth. The radio frequency circuitry 604 may communicate with other terminals via at least one wireless communication protocol. The wireless communication protocols include, but are not limited to: metropolitan area networks, various generation mobile communication networks (2G, 3G, 4G, and 5G), Wireless local area networks, and/or WiFi (Wireless Fidelity) networks. In some embodiments, the rf circuit 604 may further include NFC (Near Field Communication) related circuits, which are not limited in this application.
The display 605 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. When the display screen 605 is a touch display screen, the display screen 605 also has the ability to capture touch signals on or over the surface of the display screen 605. The touch signal may be input to the processor 601 as a control signal for processing. At this point, the display 605 may also be used to provide virtual buttons and/or a virtual keyboard, also referred to as soft buttons and/or a soft keyboard. In some embodiments, the display 605 may be one, disposed on the front panel of the terminal 600; in other embodiments, the display 605 may be at least two, respectively disposed on different surfaces of the terminal 600 or in a folded design; in other embodiments, the display 605 may be a flexible display disposed on a curved surface or a folded surface of the terminal 600. Even more, the display 605 may be arranged in a non-rectangular irregular pattern, i.e., a shaped screen. The Display 605 may be made of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), and the like.
The camera assembly 606 is used to capture images or video. Optionally, camera assembly 606 includes a front camera and a rear camera. Generally, a front camera is disposed at a front panel of the terminal, and a rear camera is disposed at a rear surface of the terminal. In some embodiments, the number of the rear cameras is at least two, and each rear camera is any one of a main camera, a depth-of-field camera, a wide-angle camera and a telephoto camera, so that the main camera and the depth-of-field camera are fused to realize a background blurring function, and the main camera and the wide-angle camera are fused to realize panoramic shooting and VR (Virtual Reality) shooting functions or other fusion shooting functions. In some embodiments, camera assembly 606 may also include a flash. The flash lamp can be a single-color temperature flash lamp or a double-color temperature flash lamp. The double-color-temperature flash lamp is a combination of a warm-light flash lamp and a cold-light flash lamp, and can be used for light compensation at different color temperatures.
The audio circuitry 607 may include a microphone and a speaker. The microphone is used for collecting sound waves of a user and the environment, converting the sound waves into electric signals, and inputting the electric signals to the processor 601 for processing or inputting the electric signals to the radio frequency circuit 604 to realize voice communication. For the purpose of stereo sound collection or noise reduction, a plurality of microphones may be provided at different portions of the terminal 600. The microphone may also be an array microphone or an omni-directional acquisition microphone. The speaker is used to convert electrical signals from the processor 601 or the radio frequency circuit 604 into sound waves. The loudspeaker can be a traditional film loudspeaker or a piezoelectric ceramic loudspeaker. When the speaker is a piezoelectric ceramic speaker, the speaker can be used for purposes such as converting an electric signal into a sound wave audible to a human being, or converting an electric signal into a sound wave inaudible to a human being to measure a distance. In some embodiments, audio circuitry 607 may also include a headphone jack.
The positioning component 608 is used for positioning the current geographic Location of the terminal 600 to implement navigation or LBS (Location Based Service). The Positioning component 608 can be a Positioning component based on the united states GPS (Global Positioning System), the chinese beidou System, the russian graves System, or the european union's galileo System.
A power supply 609 is used to supply power to the various components in terminal 600. The power supply 609 may be ac, dc, disposable or rechargeable. When the power supply 609 includes a rechargeable battery, the rechargeable battery may support wired or wireless charging. The rechargeable battery can also be used to support fast charge technology.
In some embodiments, the terminal 600 also includes one or more sensors 610. The one or more sensors 610 include, but are not limited to: acceleration sensor 611, gyro sensor 612, pressure sensor 613, fingerprint sensor 614, optical sensor 615, and proximity sensor 616.
The acceleration sensor 611 may detect the magnitude of acceleration in three coordinate axes of the coordinate system established with the terminal 600. For example, the acceleration sensor 611 may be used to detect components of the gravitational acceleration in three coordinate axes. The processor 601 may control the display screen 605 to display the user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 611. The acceleration sensor 611 may also be used for acquisition of motion data of a game or a user.
The gyro sensor 612 may detect a body direction and a rotation angle of the terminal 600, and the gyro sensor 612 and the acceleration sensor 611 may cooperate to acquire a 3D motion of the user on the terminal 600. The processor 601 may implement the following functions according to the data collected by the gyro sensor 612: motion sensing (such as changing the UI according to a user's tilting operation), image stabilization at the time of photographing, game control, and inertial navigation.
Pressure sensors 613 may be disposed on the side bezel of terminal 600 and/or underneath display screen 605. When the pressure sensor 613 is disposed on the side frame of the terminal 600, a user's holding signal of the terminal 600 can be detected, and the processor 601 performs left-right hand recognition or shortcut operation according to the holding signal collected by the pressure sensor 613. When the pressure sensor 613 is disposed at the lower layer of the display screen 605, the processor 601 controls the operability control on the UI interface according to the pressure operation of the user on the display screen 605. The operability control comprises at least one of a button control, a scroll bar control, an icon control and a menu control.
The fingerprint sensor 614 is used for collecting a fingerprint of a user, and the processor 601 identifies the identity of the user according to the fingerprint collected by the fingerprint sensor 614, or the fingerprint sensor 614 identifies the identity of the user according to the collected fingerprint. Upon identifying that the user's identity is a trusted identity, the processor 601 authorizes the user to perform relevant sensitive operations including unlocking the screen, viewing encrypted information, downloading software, paying, and changing settings, etc. The fingerprint sensor 614 may be disposed on the front, back, or side of the terminal 600. When a physical button or vendor Logo is provided on the terminal 600, the fingerprint sensor 614 may be integrated with the physical button or vendor Logo.
The optical sensor 615 is used to collect the ambient light intensity. In one embodiment, processor 601 may control the display brightness of display screen 605 based on the ambient light intensity collected by optical sensor 615. Specifically, when the ambient light intensity is high, the display brightness of the display screen 605 is increased; when the ambient light intensity is low, the display brightness of the display screen 605 is adjusted down. In another embodiment, the processor 601 may also dynamically adjust the shooting parameters of the camera assembly 606 according to the ambient light intensity collected by the optical sensor 615.
A proximity sensor 616, also known as a distance sensor, is typically provided on the front panel of the terminal 600. The proximity sensor 616 is used to collect the distance between the user and the front surface of the terminal 600. In one embodiment, when proximity sensor 616 detects that the distance between the user and the front face of terminal 600 gradually decreases, processor 601 controls display 605 to switch from the bright screen state to the dark screen state; when the proximity sensor 616 detects that the distance between the user and the front face of the terminal 600 is gradually increased, the display 605 is controlled by the processor 601 to switch from the breath-screen state to the bright-screen state.
Those skilled in the art will appreciate that the configuration shown in fig. 6 is not limiting of terminal 600 and may include more or fewer components than shown, or some components may be combined, or a different arrangement of components may be used.
The computer device in this embodiment of the present application may be provided as a server, fig. 7 is a schematic structural diagram of a server provided in this embodiment of the present application, and the server 700 may generate a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 701 and one or more memories 702, where the memory 702 stores at least one instruction, and the at least one instruction is loaded and executed by the processors 701 to implement the method for providing dish information display in each method embodiment described above. Of course, the server may also have components such as a wired or wireless network interface, a keyboard, and an input/output interface, so as to perform input/output, and the server may also include other components for implementing the functions of the device, which are not described herein again.
In an exemplary embodiment, there is also provided a computer-readable storage medium, such as a memory, including instructions executable by a processor in a terminal to perform the dish information presentation method in the following embodiments. For example, the computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims (12)

1. A method for displaying dish information is characterized by comprising the following steps:
in response to a dish information acquisition request of a terminal to a target merchant, acquiring dish sales information, dish price information, dish order information, dish inventory information and dish preference information of a plurality of dishes of the target merchant;
determining target prediction results of the plurality of dishes based on dish sales information, dish price information, dish order information, dish inventory information and dish preference information of the plurality of dishes, wherein the target prediction results are used for expressing resource quantity obtained based on sales volume of the dishes, and the resource quantity is used for expressing return of income corresponding to the sales volume;
determining a dish information display sequence of the plurality of dishes based on the target prediction results of the plurality of dishes;
and returning the dish information display sequence of the plurality of dishes to the terminal for displaying.
2. The method of claim 1, wherein determining the goal prediction results for the plurality of dishes based on dish sales information, dish price information, dish order information, dish inventory information, and dish offer information for the plurality of dishes comprises:
inputting the dish selling information, the dish price information, the dish order information, the dish inventory information and the dish preference information of the dishes into a target model for each dish in the plurality of dishes, and performing feature extraction on the dish selling information, the dish price information, the dish order information, the dish inventory information and the dish preference information through the target model to obtain dish selling features, dish price features, dish order features, dish inventory features and dish preference features;
splicing the dish selling characteristic, the dish price characteristic, the dish order characteristic, the dish inventory characteristic and the dish preference characteristic to obtain a target dish characteristic;
predicting the target dish features to obtain resource quantity features corresponding to the target dish features;
and determining a target prediction result of the dish based on the resource quantity characteristics.
3. The method of claim 2, wherein the determining of the target model comprises:
acquiring historical dish sales information, historical dish price information, historical dish order information, historical dish inventory information, historical dish preference information and sample resource quantity of a plurality of dishes;
and performing model training based on historical dish sales information, historical dish price information, historical dish order information, historical dish inventory information, historical dish preference information and sample resource quantity of the plurality of dishes to obtain the target model.
4. The method of claim 3, wherein the performing model training based on historical dish sales information, historical dish price information, historical dish order information, historical dish inventory information, historical dish offer information, and sample resource quantities of the plurality of dishes to obtain the target model comprises:
in an iteration process, inputting the historical dish sales information, the historical dish price information, the historical dish order information, the historical dish inventory information and the historical dish discount information into an initial model to obtain a training result of the iteration process;
determining a loss function based on the training result of the iteration process and the sample resource quantity;
and adjusting the model parameters of the initial model based on the loss function until the training meets the target condition, and acquiring the model corresponding to the iterative process meeting the target condition as the target model.
5. The method of claim 3, wherein the training process of the target model comprises:
and performing model training based on historical dish sales information, historical dish price information, historical dish order information, historical dish inventory information, historical dish preference information, sample resource quantity of the plurality of dishes and a linear regression algorithm to obtain the target model.
6. The method of claim 3, wherein the training process of the target model comprises:
and performing model training based on historical dish sales information, historical dish price information, historical dish order information, historical dish inventory information, historical dish preference information, sample resource quantity of the plurality of dishes and a deep learning algorithm to obtain the target model.
7. The method of claim 3, wherein the training process of the target model comprises:
and performing model training based on historical dish sales information, historical dish price information, historical dish order information, historical dish inventory information, historical dish preference information, sample resource quantity of the plurality of dishes and a clustering algorithm to obtain the target model.
8. The method of claim 3, wherein the training process of the target model comprises any one of:
performing model training based on historical dish sales information, historical dish price information, historical dish order information, historical dish inventory information, historical dish preference information, sample resource quantity of the plurality of dishes and an online algorithm to obtain the target model;
and performing model training based on historical dish sales information, historical dish price information, historical dish order information, historical dish inventory information, historical dish preference information, sample resource quantity of the plurality of dishes and an offline algorithm to obtain the target model.
9. The method of claim 1, wherein determining the order of presentation of the dish information for the plurality of dishes based on the goal predictions for the plurality of dishes comprises:
and determining the dish information display sequence of the plurality of dishes based on the numerical values of the target prediction results of the plurality of dishes.
10. A device for displaying information on dishes, said device comprising:
the acquisition module is used for responding to a dish information acquisition request of a terminal to a target merchant and acquiring dish sales information, dish price information, dish order information, dish inventory information and dish preference information of a plurality of dishes of the target merchant;
the prediction result determining module is used for determining target prediction results of the plurality of dishes based on dish sales information, dish price information, dish order information, dish inventory information and dish preference information of the plurality of dishes, wherein the target prediction results are used for expressing the quantity of resources obtained based on the sales volume of the dishes, and the quantity of the resources is used for expressing return benefits corresponding to the sales volume;
the display sequence determining module is used for determining the display sequence of the dish information of the plurality of dishes based on the target prediction results of the plurality of dishes;
and the returning module is used for returning the dish information display sequence of the plurality of dishes to the terminal for displaying.
11. A computer device, characterized in that the computer device comprises a processor and a memory, wherein at least one instruction is stored in the memory, and the instruction is loaded and executed by the processor to implement the operation performed by the dish information presentation method according to any one of claims 1 to 9.
12. A computer-readable storage medium, wherein at least one instruction is stored in the storage medium, and the instruction is loaded and executed by a processor to implement the operations performed by the dish information displaying method according to any one of claims 1 to 9.
CN202010789396.8A 2020-08-07 2020-08-07 Dish information display method and device, computer equipment and storage medium Active CN112000264B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010789396.8A CN112000264B (en) 2020-08-07 2020-08-07 Dish information display method and device, computer equipment and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010789396.8A CN112000264B (en) 2020-08-07 2020-08-07 Dish information display method and device, computer equipment and storage medium

Publications (2)

Publication Number Publication Date
CN112000264A CN112000264A (en) 2020-11-27
CN112000264B true CN112000264B (en) 2022-06-07

Family

ID=73462917

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010789396.8A Active CN112000264B (en) 2020-08-07 2020-08-07 Dish information display method and device, computer equipment and storage medium

Country Status (1)

Country Link
CN (1) CN112000264B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022211726A1 (en) * 2021-04-01 2022-10-06 Pt Goto Gojek Tokopedia Methods and systems for generating recommendations for menu card data
CN114857677A (en) * 2022-03-01 2022-08-05 青岛海尔空调器有限总公司 Method and device for controlling air conditioner and air conditioner

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20070117379A (en) * 2006-06-08 2007-12-12 홍성우 System of automatic ordering for a dining room and method thereof
CN104794660A (en) * 2014-01-20 2015-07-22 中国移动通信集团公司 Electronic ordering method, ordering server and electronic ordering system
CN107203950A (en) * 2016-03-18 2017-09-26 湖南餐启科技有限公司 A kind of vegetable recommends method and system
CN107230131A (en) * 2017-02-06 2017-10-03 北京三快在线科技有限公司 A kind of vegetable recommends method, apparatus and system
CN108288219A (en) * 2018-03-19 2018-07-17 口口相传(北京)网络技术有限公司 The method for pushing and system of food product information

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20070117379A (en) * 2006-06-08 2007-12-12 홍성우 System of automatic ordering for a dining room and method thereof
CN104794660A (en) * 2014-01-20 2015-07-22 中国移动通信集团公司 Electronic ordering method, ordering server and electronic ordering system
CN107203950A (en) * 2016-03-18 2017-09-26 湖南餐启科技有限公司 A kind of vegetable recommends method and system
CN107230131A (en) * 2017-02-06 2017-10-03 北京三快在线科技有限公司 A kind of vegetable recommends method, apparatus and system
CN108288219A (en) * 2018-03-19 2018-07-17 口口相传(北京)网络技术有限公司 The method for pushing and system of food product information

Also Published As

Publication number Publication date
CN112000264A (en) 2020-11-27

Similar Documents

Publication Publication Date Title
CN111652678B (en) Method, device, terminal, server and readable storage medium for displaying article information
CN111476306B (en) Object detection method, device, equipment and storage medium based on artificial intelligence
CN112162671B (en) Live broadcast data processing method and device, electronic equipment and storage medium
CN110471858B (en) Application program testing method, device and storage medium
CN111506758B (en) Method, device, computer equipment and storage medium for determining article name
CN111897996B (en) Topic label recommendation method, device, equipment and storage medium
CN111737573A (en) Resource recommendation method, device, equipment and storage medium
CN111970523A (en) Information display method, device, terminal, server and storage medium
CN111078521A (en) Abnormal event analysis method, device, equipment, system and storage medium
CN112000264B (en) Dish information display method and device, computer equipment and storage medium
CN111126925A (en) Method and device for determining replenishment quantity of front bin, computer equipment and storage medium
WO2021218634A1 (en) Content pushing
CN113891166A (en) Data processing method, data processing device, computer equipment and medium
CN112766406A (en) Article image processing method and device, computer equipment and storage medium
CN113469779A (en) Information display method and device
CN111754272A (en) Advertisement recommendation method, recommended advertisement display method, device and equipment
CN113987326B (en) Resource recommendation method and device, computer equipment and medium
CN112230822B (en) Comment information display method and device, terminal and storage medium
CN112989198B (en) Push content determination method, device, equipment and computer-readable storage medium
CN114385854A (en) Resource recommendation method and device, electronic equipment and storage medium
CN112948690A (en) Search method, device, equipment and storage medium
CN111967420A (en) Method, device, terminal and storage medium for acquiring detailed information
CN112116480A (en) Virtual resource determination method and device, computer equipment and storage medium
CN111143441A (en) Gender determination method, device, equipment and storage medium
CN115086774B (en) Resource display method and device, electronic equipment and storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant